Global asymptotic synchronization of coupled interconnected recurrent neural networks via pinning control

Authors:
Zhanshan Wang;Dakai Zhou;Dongsheng Ma;Shuxian Lun
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;College of Engineering, Bohai University, Jinzhou, People's Republic of China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

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Pinning a complex delayed dynamical network to a homogenous trajectory

IEEE Transactions on Circuits and Systems II: Express Briefs

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Abstract

Global asymptotic synchronization problem of a coupled interconnected recurrent neural networks with linearly delayed coupled has been investigated. By using the state feedback and delayed state feedback pinning control method, two different pinning synchronization criteria have been established. One is based on the matrix eigenvalue of the coupled matrix, the other is based on the matrix inequality of the known networks information. Some remarks on the synchronization criteria are used to show the characteristics of the proposed results.